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In this section, we will delve into the core components of Hadoop, which form the backbone of the Hadoop ecosystem. Understanding these components is crucial for anyone looking to work with Hadoop, as they provide the fundamental functionalities that enable Hadoop to process and store large datasets efficiently.

Key Concepts

Hadoop Distributed File System (HDFS)
MapReduce
YARN (Yet Another Resource Negotiator)

Hadoop Distributed File System (HDFS)

HDFS is the primary storage system used by Hadoop applications. It is designed to store large datasets reliably and to stream those datasets at high bandwidth to user applications.

Features of HDFS:

Scalability: HDFS can scale to thousands of nodes and petabytes of data.
Fault Tolerance: Data is replicated across multiple nodes to ensure reliability.
High Throughput: Designed for high throughput rather than low latency.

HDFS Architecture:

NameNode: Manages the metadata and namespace of the file system.
DataNode: Stores the actual data blocks.
Secondary NameNode: Performs housekeeping functions for the NameNode.

Example:

Imagine you have a 1GB file. HDFS will split this file into smaller blocks (default 128MB) and distribute these blocks across different DataNodes. The NameNode keeps track of where each block is stored.

MapReduce

MapReduce is a programming model and an associated implementation for processing and generating large datasets. It divides the task into two main functions: Map and Reduce.

Features of MapReduce:

Parallel Processing: Tasks are divided and processed in parallel.
Scalability: Can handle large datasets by distributing the workload.
Fault Tolerance: Automatically handles failures by reassigning tasks.

MapReduce Workflow:

Map Function: Processes input data and produces key-value pairs.
Shuffle and Sort: Organizes the key-value pairs for the Reduce function.
Reduce Function: Aggregates the key-value pairs to produce the final output.

Example:

// Pseudo-code for a simple MapReduce job to count word occurrences
public class WordCount {
    public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
        public void map(LongWritable key, Text value, Context context) {
            String[] words = value.toString().split(" ");
            for (String word : words) {
                context.write(new Text(word), new IntWritable(1));
            }
        }
    }

    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
        public void reduce(Text key, Iterable<IntWritable> values, Context context) {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            context.write(key, new IntWritable(sum));
        }
    }
}

YARN (Yet Another Resource Negotiator)

YARN is the resource management layer of Hadoop. It allows multiple data processing engines such as MapReduce, Tez, and Spark to handle data stored in a single platform, unlocking the potential for a broader array of data processing applications.

Features of YARN:

Resource Management: Manages resources across the cluster.
Job Scheduling: Schedules and monitors jobs.
Multi-tenancy: Supports multiple applications running simultaneously.

YARN Architecture:

ResourceManager: Manages resources and schedules applications.
NodeManager: Manages resources on a single node.
ApplicationMaster: Manages the lifecycle of applications.

Example:

When a job is submitted, the ResourceManager allocates resources and the ApplicationMaster manages the execution of the job across the cluster.

Summary

In this section, we covered the core components of Hadoop:

HDFS: The storage layer that provides scalable and fault-tolerant storage.
MapReduce: The processing layer that allows for parallel processing of large datasets.
YARN: The resource management layer that enables efficient resource allocation and job scheduling.

Understanding these components is essential for effectively working with Hadoop and leveraging its full potential. In the next section, we will dive deeper into HDFS and explore its architecture and commands.

Hadoop Core Components

Key Concepts

Hadoop Distributed File System (HDFS)

Features of HDFS:

HDFS Architecture:

Example:

MapReduce

Features of MapReduce:

MapReduce Workflow:

Example:

YARN (Yet Another Resource Negotiator)

Features of YARN:

YARN Architecture:

Example:

Summary

Hadoop Course

Module 1: Introduction to Hadoop

Module 2: Hadoop Architecture

Module 3: HDFS (Hadoop Distributed File System)

Module 4: MapReduce Programming

Module 5: Hadoop Ecosystem Tools

Module 6: Advanced Hadoop Concepts

Module 7: Real-World Applications and Case Studies

Module 8: Hands-On Projects